Majlesi Journal of Electrical Engineering <p>The scope of MJEE is ranging from mathematical foundation to practical engineering design in all areas of electrical engineering. The editorial board is international and original unpublished papers are welcome. The journal is devoted primarily to research papers, but very high quality survey and tutorial papers are also published. There is no publication charges for non-Iranian authors.</p> en-US (Dr. Hossein Emami) (Support Contact) Thu, 08 Sep 2022 04:24:33 +0000 OJS 60 Topology optimization of cyber network impacts on smart grid adequacy evaluation considering cyber-power interdependency <p class="MJEE-Abstract">Smart grid is comprised of two distinct and intricate cyber-power networks. In smart grids, cyber networks are employed to control, monitor, and protect various kinds of physical structures. Most of proposed methods have considered that cyber components will not fail. This clarification causes error in reliability study; namely in smart grids with advanced cyber network topologies. Based on how cyber failure affects the power system, cyber and power networks may have direct or indirect interdependency. This paper introduces a new analytical reliability assessment methodology, which considers impact of direct cyber network failures on power networks, effectively. The proposed method evaluates the smart grid reliability while taking power and cyber component (monitoring/control/protection devices) failures into account. In this paper, a new procedure based on changing cyber topology structure is suggested. In addition, an applicable cyber network structure is found and employed to improve smart grid reliability. The proposed method is applied to a realistic distribution system in Iran. The results prove that the study of both cyber and power effects on reliability assessment of smart grid are essential to be carried out by system operators. Therefore, an optimized cyber network configuration is introduced as a reliability improvement method.</p> Hossein Askarian Abyaneh ##submission.copyrightStatement## Tue, 16 Nov 2021 00:00:00 +0000 IMPACT OF THE PENETRATION OF RENEWABLE ENERGY ON DISTRIBUTED GENERATION SYSTEMS <p><em>As the proportion of total generation by renewable sources compared to non-renewable sources increases, the relative inertial stability provided by large rotating generators in electricity grids is found to shrink and is not being replaced by sources such as photovoltaic and wind power, which are already known for their inherent variability. This leads to electricity generation systems being less stable, less flexible, and less adequate in applications with a high diversity factor, and literature shows that the penetration of renewable energy sources in distribution-generation/microgrid system frequently presents several technical and economic challenges in their usual applications. This work examines how increased renewable energy penetration impacts the distribution-generation system and suggests approaches and measures for tackling the challenges that are associated with it.</em></p> Oyinlolu Ayomidotun Odetoye, Oghenewvogaga James Oghorada, Adeleke Olusola Alimi, Babatunde Adetokun, Uchenna N Okeke, Paul K Olulope, Dr., Matthew O. Olanrewaju, John O. Onyemenam, John O. Onyemenam ##submission.copyrightStatement## Sun, 28 Aug 2022 00:00:00 +0000 Filtering Techniques To Reduce Speckle Noise And Image Quality Enhancement Methods On Porous Silicon Images Layers <p>Recently, many studies have examined filters for reducing or remove speckle noise, which is inherent to different images types such as Porous Silicon (PS) images, in order to ameliorate the metrological evaluation of their applications. In the case of digital images, noise can produce difficulties in the diagnosis of images details, such as edges and limits, should be preserved. Most algorithms can reduce or remove speckle noise, but they do not consider the conservation of these details. This paper describes in detail, the different techniques that focus mainly on the smoothing or elimination of speckle noise in images, as the aim of this study is to achieve the improvement of this smoothing and elimination, which is directly related to different processes (such as the detection of interest regions). Furthermore, the description of these techniques facilitates the operations of evaluations and research with a more specific scope. This study initially covers the definition and modeling of speckle noise. Then we elaborated in detail the different types of filters used in this study, Finally, five statistical parameters such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Structural Similarity Index (SSIM), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR)&nbsp; are calculated, compared and the results are tabulated, common in filter evaluation processes. Trough the calculation of the statistical parameters, we can classify the filters in terms of perceptual quality by providing greater certainty.</p> Tifouti Issam, Rahmouni Salah, Meriane Brahim ##submission.copyrightStatement## Sun, 28 Aug 2022 00:00:00 +0000 Automatic Diagnosis of Breast Cancer in Histopathologic Images Based on Convolutional AutoEncoders and Reinforced Feature Selection <p>Breast cancer is one the most ubiquitous types of cancer which affect a considerable number of women around the globe. It is a malignant tumor, whose origin is in the glandular epithelium of the breast and causes serious health-related problems for patients. Although there is no known way of curing this disease, early detection of it can be very fruitful in terms of reducing the negative ramifications. Thus, accurate diagnosis of breast cancer based on automatic approaches is demanded immediately. Computer vision-based techniques in the analysis of medical images, especially histopathological images, have proved to be extremely performant. In this paper, we propose a novel approach for classifying malignant or non-malignant images. Our approach is based on the latent space embeddings learned by convolutional autoencoders. This network takes a histopathological image and learns to reconstruct it and by compressing the input into the latent space, we can obtain a compressed representation of the input. These embeddings are fed to a reinforcement learning-based feature selection module which extracts the best features for distinguishing the normal from the malicious images. We have evaluated our approach on a well-known dataset, named BreakHis, and used the K-Fold Cross Validation technique to obtain more reliable results. The accuracy, achieved by the proposed model, is 96.8% which exhibits great performance.</p> Ali Abdulhussain Fadhil, Miaad Adnan, Hamza Radhi, Mahmood Al-Mualm, Mahmood Hasen Alubaidy, Mohamed Salih, Sarah Jaafar Saadoon ##submission.copyrightStatement## Mon, 19 Sep 2022 08:45:30 +0000 Low Power Broadband sub-GHz CMOS LNA with 1 GHz Bandwidth for IoT Applications <p>This paper presents a broadband low-power CMOS low noise amplifier (LNA) in 130 nm technology for sub-GHz Internet of Things (IoT) applications. The proposed circuit consists of a current reuse common source amplifier (CSA) in the forward path, and a positive simple transconductance amplifier (PSTA) in the feedback path. Theoretical calculation of the input admittance shows a positive part that presents a parallel inductance. This equivalent parallel inductance in the input can cancel out the input capacitance of CSA and electrostatic discharge (ESD) pad, enhancing the frequency bandwidth in the sub-GHz frequency band. Post-layout simulated including ESD pads and package model in 130 nm CMOS technology, LNA achieves a voltage gain of 16.5 dB in a frequency bandwidth of 50 MHz to 1.1 GHz, noise figure (NF) of less than 2.4 dB, input return loss (S11) of -11 dB, input third order intercept point (IIP3) of -11 dBm and 1 mW power consumption from a 1 V power supply, showing a good figure of merit compared to other works. The occupied core area is less than 0.002 mm<sup>2</sup>.</p> Sayed Vahid Mir-Moghtadaei, Farshad Shirani Bidabadi ##submission.copyrightStatement## Tue, 20 Sep 2022 05:51:21 +0000 Electricity Demand Prediction by a Transformer-Based Model <p>The frighteningly high levels of power consumption at present are caused mainly by the expanding global population and the accessibility of energy-hungry smart technologies. So far, various simulation tools, engineering- and AI-based methodologies have been utilized to anticipate power consumption effectively. While engineering approaches forecast using dynamic equations, AI-based methods forecast using historical data. The modeling of nonlinear electrical demand patterns is still lacking for durable solutions, however, as the available approaches are only effective for resolving transient dependencies. Furthermore, because they are only based on historical data, the current methodologies are static in nature. In this research, we present a system based on deep learning to anticipate power consumption while accounting for long-term historical relationships. In our approach, a transformer-based model is used for the prediction of electricity demand on data collected from the regional facilities in Iraq. According to the conducted experiments, our approach claims competitive performance, achieving an error rate of 2.0 in predicting 1-day-ahead of electricity demand in the test samples.</p> Ahmed Mohammed Mahmood, Musaddak Maher Abdul Zahra, Waleed Hamed, Bashar S. Bashar, Alaa Hussein Abdulaal, Taif Alawsi, Ali Hussein Adhab ##submission.copyrightStatement## Sat, 24 Sep 2022 09:36:43 +0000 Improving Performance of the Convolutional Neural Networks for Electricity Theft Detection by Using Cheetah Optimization Algorithm <p>This paper presents an efficient approach to improve convolutional neural network(CNN) performance using cheetah optimization algorithm (CHOA). The main challenges of these networks are optimizing the parameters and finding an efficient architecture. Network performance and achieving efficient learning models based on a particular problem, depend on adjusting values of the hyper-parameters and requiring exploring in a complex and large search space. In order to solve these types of problems, heuristic-based searches are used. Therefore, the main idea of this paper is using CHOA algorithm to adjust the optimal hyper-parameters of CNN networks including number of convolutional and pooling layers, number of filters per convolutional layer and their size, stride of each filter, pool size and stride of each pooling layer and Batch size. This paper presents an optimal approach to increase the detection rate of CNN network that abnormal samples are generated and clustered by artificial attacks and CHOA algorithm, respectively. &nbsp;The resulting architecture is evaluated on the ISSDA dataset. Based on the obtained results, the proposed method with high detection rate identifies unauthorized electricity customers.</p> hassan ghaedi, Seyed Reza Kamel Tabbakh, reza ghaemi ##submission.copyrightStatement## Fri, 30 Sep 2022 12:40:18 +0000 An Ensemble Learning Approach for Glaucoma Detection in Retinal Images <p>To stop vision loss from glaucoma, early identification and regular screening are crucial. Convolutional neural networks (CNN) have been effectively used in recent years to diagnose glaucoma automatically from color fundus pictures. CNNs can extract distinctive characteristics directly from the fundus pictures, as opposed to the current automatic screening techniques. In this study, a CNN-based deep learning architecture is created for the categorization of normal and glaucomatous fundus pictures. In this paper, we propose a deep learning-based framework for the detection of glaucoma based on retinal images. Our proposed approach utilizes the two CNN-based models, namely Inception and DenseNet, in order to classify the input images. We also show the impact of transfer learning on the training and the validation processes and put forward an effective pipeline with lower trainable parameters for the target task. Our experiments on a collected dataset demonstrate the efficacy of the proposed model by achieving an accuracy of 93.84%, a precision of 92.83%, and a recall of 95.00%.</p> Marwah M. Mahdi, Mohammed Abdulkreem Mohammed, Haider Al-Chalibi; Bashar S. Bashar; Hayder Adnan Sadeq, Talib Mohammed Jawad Abbas ##submission.copyrightStatement## Sun, 02 Oct 2022 10:04:27 +0000 Malware Detection using Deep Neural Networks on Imbalanced Data <p class="MJEE-Abstract">Through the use of malware, particularly JavaScript, cybercriminals have turned online applications into one of their main targets for impersonation. Detection of such dangerous code in real-time, therefore, becomes crucial in order to prevent any harmful action. By categorizing the salient characteristics of the malicious code, this study suggests an effective technique for identifying malicious Java scripts that were previously unknown, employing an interceptor on the client side. By employing the wrapper approach for dimensionality reduction, a feature subset was generated. In this paper, we propose an approach for handling the malware detection task in imbalanced data situations. Our approach utilizes two main imbalanced solutions namely, Synthetic Minority Over Sampling Technique (SMOTE) and Tomek Links with the object of augmenting the data and then applying a Deep Neural Network (DNN) for classifying the scripts. The conducted experiments demonstrate the efficient performance of our approach and it achieves an accuracy of 94.00%.&nbsp;</p> Mohammed Abdulkreem Mohammed, Drai Ahmed Smait, Mustafa Al-Tahai, Israa S. Kamil, Kadhum Al-Majdi, Shahad K. Khaleel ##submission.copyrightStatement## Sun, 02 Oct 2022 10:04:34 +0000 A Transformer-based approach for anomaly detection in wire electrical discharge <p class="MJEE-Abstract">Although theoretical models of manufacturing processes are useful for understanding physical events, it can be challenging to apply them in real-world industrial settings. When huge data are accessible, artificial intelligence approaches in the context of Industry 4.0 can offer effective answers to real production challenges. Deep learning is increasingly being used in the realm of artificial intelligence to address a variety of issues relating to information and communication technology, but it is still limited or perhaps nonexistent in the industrial sector. In this study, wire electrical discharge machining—a sophisticated machining technique primarily used for computer hardware components—is applied to effectively forecast unforeseen occurrences. By identifying hidden patterns in process signals, anomalies, such as changes in the thickness of a machined item, may be efficiently anticipated before they occur. In this study, a model for anomaly detection in the sequence of thickness change in the machined component based on transformers is suggested. Our method is able to achieve 94.32 % and 94.16 % accuracy in Z 135 and Z 15 datasets, respectively. Also, it forecasts the abnormalities inside the sequence 1.1 seconds in advance, according to our tests on a dataset that has been introduced.</p> Waleed Hammed, Ameer H. Al-Rubaye, Bashar S. Bashar, Merzah Kareem Imran, Mustafa Ghanim Rzooki, Ali Mohammed Hashesh ##submission.copyrightStatement## Sun, 09 Oct 2022 06:09:29 +0000 An Adaptive Un-Sharp Masking Method for Contrast Enhancement in Images with Non-uniform Blur <p>Un-sharp masking method improves the images contrast without requiring any prior knowledge. In this method, a sharper image can be achieved by empowering the high frequency components of the input image. Un-sharp masking has a parameter named gain factor which has a high effect on the enhanced image quality. In this paper, an approach is proposed to adaptively estimate the appropriate value of this parameter in order to effectively enhance an image with local blur, or an image with non-uniform blur. In proposed method, first, the input image is segmented into blur and non-blur regions. Then the gain factor is estimated for each region adaptively. In this approach, the influence of the image blurriness on its gradient information is used to estimate the value for the gain factor. The image quality assessments are applied to evaluate the performance of proposed un-sharp masking method in image enhancement. Experimental results demonstrate that the performance of our proposed method is better than the performance of existing un-sharp masking methods in image enhancement.</p> zahra mortezaie, Hamid Hassanpour, Sekine Asadi Amiri ##submission.copyrightStatement## Fri, 14 Oct 2022 13:36:07 +0000 Investigation of Stator Windings Looseness of Polyphase Induction Machine after Rewinded in Workshop: Numerical and Experimental Analysis <p>Electrical machines, especially induction motors, are broadly employed in industry as they need low maintenance. In this article, the Experimental Modal Analysis (EMA) test plays a significant role in evaluating electrical machine vibrations. The hammer test is a popular EMA approach for locating the exact location of winding looseness. To acquire the modal parameters for validation with a theoretical and numerical model, the EMA is performed on the stator end winding structure. The EMA's Operational Deflection Shape (ODS) confirms the windings' precise deformation. The induction machine's driving end (DE) and non-driving end (NDE) windings are tested to EMA to evaluate the stator slot structure's looseness. The proposed technique was compared to the Finite Element Method (FEM) and theoretical calculations for verification.</p> V Sri Ram Prasad Kapu; Varsha Singh, Dr. ##submission.copyrightStatement## Fri, 21 Oct 2022 06:20:55 +0000 Direct Matching Antennas in RF Energy Harvesting Systems: A Review <p>A historical review of Radio Frequency Energy Harvesting (RFEH) Rectenna (Rectifier Antenna) Systems without a matching network is performed, with emphasis on the antenna part. As the antenna, matching network and rectifier are the main parts of the rectenna systems, the reasons behind the elimination of the matching network are presented and different special antennas suitable for direct matching to the rectifier, without using a matching network, are reviewed. Since the diode in the rectifier is a nonlinear element, its input impedance is changed with varying operating conditions such as input power, frequency and output load impedance of the rectifier. So, it is a challenge for researchers to match the antenna impedance directly to the rectifier in variable operational conditions.</p> Vahid Honarvar, Farzad mohajeri ##submission.copyrightStatement## Fri, 21 Oct 2022 06:21:38 +0000 Single Image Super-Resolution Enhancement using Luminance Map and Atmospheric Light Removal <p>Image enhancement is used in many image processing applications such as medical diagnostics, satellite image analysis, surveillance cameras, etc. Super resolution attempts to reconstruct high resolution images from low resolution images and it can be considered as a preprocessing step for object recognition and image classification. Various algorithms have been introduced for single-image super resolution, but these algorithms often face important challenges such as poorly matching the reconstructed image with the original image, as well as the blurring of edges and texture details. The aim of this manuscript is to introduce a preprocessing operation to improve the performance of the super resolution process in natural images. In the proposed method, the low resolution input image is enhanced before entering the resolution change module. Calculating the brightness of the pixels in the image channels, creating the luminance map and removing atmospheric light, applying the transmittance map by using the luminance coefficients, and recovering the natural image in all three color channels are the above preprocessing steps. The proposed method succeeded in increasing the PSNR parameter by 4.35%, 10.62%, and 8.31%, as well as 0.23%, 3.10%, and 7.91% of the SSIM parameter for Set5, Set14, and BSD100 datasets compared to its closest state-of-the-art methods.</p> Mohammad Amin Shayegan, Samira Poormajidi ##submission.copyrightStatement## Fri, 21 Oct 2022 06:26:12 +0000 Particle Swarm Optimization (PSO) Based MPPT controller Modeling and Design of Photovoltaic Modules <p>Photovoltaic (PV) panel produces electricity depending on a variety of characteristics, including the PV module model, design specifications, and ambient circumstances such as temperature and sun irradiation. To analyze and model the effect of these factors on PV performance, a PV model is significant to be studied and modeled in advance. It is desirable to be compatible with the real-physical behavior of the PV panel.<br>This paper presents mathematical modeling, design, and simulation of the three-diode model (3DM) MPPT controller instead of using conventional single/double diode PV models. The proposed PV model is analyzed, verified, and simulated at various temperature and irradiance levels. Furthermore, Particle Swarm Optimization (PSO) as a multi-objective algorithm is used for the Maximum Power Point Tracking MPPT controller to enhance the performance of the module and PV array system. A DC/DC boost converter is combined with the proposed 3DM model and connected through a resistive load. Results show that adopting PSO-based MPPT improves the performance of the PV panel compared to the traditional MPPT and verified the theoretical background.</p> Roshna A fuad, Diary R. Sulaiman ##submission.copyrightStatement## Sat, 22 Oct 2022 18:16:24 +0000 Fire Detection and Verification Using Convolutional Neural Networks, Masked Autoencoder and Transfer Learning <p>Wildfire detection is a time-critical application since it can be challenging to identify the source of ignition in a short amount of time, which frequently causes the intensity of fire incidents to increase. The development of precise early-warning applications has sparked significant interest in expert systems research due to this issue, and recent advances in deep learning for challenging visual interpretation tasks have created new study avenues. In recent years the power of deep learning-based models sparked the researcher’s interests from a variety of fields. Specially, convolutional neural networks (CNN) have become the most suited approach for computer vision tasks. As a result, in this paper we propose a CNN-based pipeline for classifying and verifying fire-related images. Our approach consists of two models, first of which classifies the input data and then the second model verifies the decision made by the first one by learning more robust representations obtained from a large masked auto encoder-based model. The verification step boosts the performance of the classifier with respect to false positives and false negatives. Based on extensive experiments, our approach proves to improve previous state-of-the-art algorithms by 3 to 4% in terms of accuracy.</p> Zainab Abed Almoussawi, Raed Khalid, Zahraa Salam Obaid, Zuhair I. Al Mashhadani, Kadhum Al-Majdi, Refad E. Alsaddon, Hassan Mohammed Abed ##submission.copyrightStatement## Mon, 07 Nov 2022 06:42:57 +0000 Design of a Compact 900 MHz Class-F Power Amplifier with Efficiency Improvement Using Modified Harmonic Control Circuit <p>A harmonic control circuit (HCC) is one of the most important block in the Class-F power amplifiers, which should pass the even harmonics and suppress the odd harmonics. Long open stubs are usually used to suppress odd harmonics in the conventional Class-F power amplifiers, which resulted in the large size of the amplifiers. In this work, two Class-F amplifiers are designed, a simple amplifier with traditional HCC and proposed PA with a proposed HCC. The designed HCC suppresses third, fifth and seventh harmonics and easily pass second, fourth and sixth harmonics. In the proposed amplifier with deigned HCC, the design parameters are improved compared to the simple Class-F with traditional HCC. The PAE, drain efficiency (DE) and gain parameters are increased from 76%, 79% and 19.4 dB to 79.2%, 82.2% and 21.2 dB, respectively. The proposed PA is fabricated, measured and results show that the proposed PA correctly works at 0.9 GHz with 0.12 GHz bandwidth.</p> Saeed Roshani, Rasoul Azadi, Arez Nosratpour, Mohammad Hazhir Mozaffari ##submission.copyrightStatement## Wed, 07 Dec 2022 08:53:38 +0000